{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this notebook, I\n",
    "- read in the height anomlies computed from Arctic DEM and convert the coordinates to lat/lon\n",
    "- filter out unrealistically large height anomlies (< 50 m)\n",
    "- save data as an h5 file for further analyis \n",
    "- make a couple of dh vs time scatterplots (both binned an unbinned)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import h5py\n",
    "from utils import transform_coord\n",
    "from utils import binning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>h_li</th>\n",
       "      <th>Z1</th>\n",
       "      <th>Z_Z1</th>\n",
       "      <th>t_year</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>699254.2079</td>\n",
       "      <td>6579142.437</td>\n",
       "      <td>1061.844116</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>11060.84412</td>\n",
       "      <td>2019.238933</td>\n",
       "      <td>-137.499895</td>\n",
       "      <td>59.304029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>699253.2196</td>\n",
       "      <td>6579122.437</td>\n",
       "      <td>1068.238525</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>11067.23853</td>\n",
       "      <td>2019.238933</td>\n",
       "      <td>-137.499931</td>\n",
       "      <td>59.303850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>699248.0569</td>\n",
       "      <td>6579002.402</td>\n",
       "      <td>1113.445068</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>11112.44507</td>\n",
       "      <td>2019.238933</td>\n",
       "      <td>-137.500132</td>\n",
       "      <td>59.302777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>699247.2329</td>\n",
       "      <td>6578982.394</td>\n",
       "      <td>1116.871948</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>11115.87195</td>\n",
       "      <td>2019.238933</td>\n",
       "      <td>-137.500165</td>\n",
       "      <td>59.302598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>699246.3969</td>\n",
       "      <td>6578962.387</td>\n",
       "      <td>1120.103271</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>11119.10327</td>\n",
       "      <td>2019.238933</td>\n",
       "      <td>-137.500198</td>\n",
       "      <td>59.302419</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0            x            y         h_li      Z1         Z_Z1  \\\n",
       "0           0  699254.2079  6579142.437  1061.844116 -9999.0  11060.84412   \n",
       "1           1  699253.2196  6579122.437  1068.238525 -9999.0  11067.23853   \n",
       "2           2  699248.0569  6579002.402  1113.445068 -9999.0  11112.44507   \n",
       "3           3  699247.2329  6578982.394  1116.871948 -9999.0  11115.87195   \n",
       "4           4  699246.3969  6578962.387  1120.103271 -9999.0  11119.10327   \n",
       "\n",
       "        t_year         lon        lat  \n",
       "0  2019.238933 -137.499895  59.304029  \n",
       "1  2019.238933 -137.499931  59.303850  \n",
       "2  2019.238933 -137.500132  59.302777  \n",
       "3  2019.238933 -137.500165  59.302598  \n",
       "4  2019.238933 -137.500198  59.302419  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('data_sample_0617.csv') #read in output for Arctic DEM\n",
    "lon, lat= transform_coord('32607','4326', df['x'].values, df['y'].values) #re-project from UTM 7 to WGS84\n",
    "#addd lat and lon to dataframe\n",
    "df['lon'] = lon\n",
    "df['lat'] = lat\n",
    "display(df.head())\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([2.19196e+05, 3.60000e+01, 0.00000e+00, 0.00000e+00, 0.00000e+00,\n",
       "        0.00000e+00, 0.00000e+00, 4.15180e+04, 1.27120e+04, 5.17400e+03]),\n",
       " array([-1062.2726  ,   428.810397,  1919.893394,  3410.976391,\n",
       "         4902.059388,  6393.142385,  7884.225382,  9375.308379,\n",
       "        10866.391376, 12357.474373, 13848.55737 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#look at distribution of elevation anomalies\n",
    "from matplotlib import pyplot as plt\n",
    "plt.hist(df['Z_Z1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "49.98547363"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filter out high anomalies\n",
    "import numpy as np\n",
    "df_filtered = df.loc[abs(df['Z_Z1']) < 50]\n",
    "len(df_filtered)\n",
    "np.max(df_filtered['Z_Z1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-a4d53f5e5a97>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#check new histogram\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_filtered\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Z_Z1'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m50\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "#check new histogram\n",
    "plt.hist(df_filtered['Z_Z1'], 50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "#write filtered data to h5 file\n",
    "f2 = h5py.File('filtered_v2.h5', 'w')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "f2['x'] = df_filtered['x']\n",
    "f2['y'] = df_filtered['y']\n",
    "f2['h_li'] = df_filtered['h_li']\n",
    "f2['dh'] = df_filtered['Z_Z1']\n",
    "f2['t_year'] = df_filtered['t_year']\n",
    "f2['lon'] = df_filtered['lon']\n",
    "f2['lat'] = df_filtered['lat']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [],
   "source": [
    "f2.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'residual elevation')"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#make a scatterplot of dh vs year\n",
    "plt.scatter(df_filtered['t_year'],  df_filtered['Z_Z1'], s = 0.1)\n",
    "plt.xlabel('t_year')\n",
    "plt.ylabel('residual elevation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda3/lib/python3.7/site-packages/pandas/core/series.py:1146: FutureWarning: \n",
      "Passing list-likes to .loc or [] with any missing label will raise\n",
      "KeyError in the future, you can use .reindex() as an alternative.\n",
      "\n",
      "See the documentation here:\n",
      "https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#deprecate-loc-reindex-listlike\n",
      "  return self.loc[key]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#bin data and compare to original scatterplot (adapted from SceinceDataGeneration tutorial)\n",
    "plt.figure()\n",
    "plt.scatter(df_filtered['t_year'],  df_filtered['Z_Z1'], s = 0.1)\n",
    "xb,yb = binning(df_filtered['t_year'], df_filtered['Z_Z1'], xmin=2018.5,xmax=2020.26, dx=0.1, window=0.25,interp=True, median=True)[0:2]\n",
    "#plt.plot(t_new,z_i_new,'.',markersize=0.1)\n",
    "plt.plot(xb, yb,'r.')\n",
    "plt.ylim([0,10])\n",
    "plt.ylabel('Elevation residual (m)')\n",
    "plt.xlabel('Time (yrs)')\n",
    "plt.savefig(\"topo_timeseries_10km.png\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1434.131836"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
